Recommendation of Fashion Coordination Posts Based on Image and Linguistic Information

Abstract

In recent years, many images and texts related to fashion coordination have been posted on social media services. It has become common for users to select their outfits based on other users' posts. Social media services store a large amount of user, image, linguistic information, etc., that are expected to be an advantage in business by the appropriate utilization. In this study, we propose a recommendation method for fashion coordination posts based on both image and linguistic similarity. Specifically, at first, Image2StyleGAN is learned to measure the image similarity, and Doc2Vec is learned to measure the linguistic information similarity. Secondly, we obtain the two types of similarity rankings by the above methods and calculate their weighted sum with an adjustable parameter. This allows us to customize which information is more important for each user. Finally, we present how to utilize the proposed method using real-world fashion coordination service application data.